Applying MDL in PSO for learning Bayesian networks

Shu Ching Kuo, Hung Jen Wang, Hsiao Yi Wei, Chih Chuan Chen, Sheng Tun Li

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Since learning Bayesian networks from data is difficult, a new approach is proposed. The particle swarm optimization (PSO) and minimum description length (MDL) are combined to obtain a suitable Bayesian network. MDL is the fitness function in this learning algorithm to evaluate the goodness of the network. By adopting MDL, the balance between simplicity and accuracy is assured, which enables the optimal solution for complex models to be found in reasonable time. Base on the MDL principle, the PSO is used to enhance the structure learning in Bayesian networks. Moreover, conditional probabilities associated with the Bayesian networks are then statistically derived from these data. In the end, the Stroke data set is used for testing the efficiency and effectiveness of the stable network. Experimental results show that the proposed approach has a good accuracy than the comparative methods.

原文English
主出版物標題FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
頁面1587-1592
頁數6
DOIs
出版狀態Published - 2011
事件2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
持續時間: 2011 六月 272011 六月 30

出版系列

名字IEEE International Conference on Fuzzy Systems
ISSN(列印)1098-7584

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
國家/地區Taiwan
城市Taipei
期間11-06-2711-06-30

All Science Journal Classification (ASJC) codes

  • 軟體
  • 理論電腦科學
  • 人工智慧
  • 應用數學

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